Introduction

Thread pooling describes a technique by which threads of execution are managed and to which work is distributed. Additional semantics such as concurrency control may also be defined. Thread pooling is a nice way to:

Manage complexity

Thread pooling is a natural fit for state based processing – if you can decompose your system into a set of state machines, thread pooling works nicely and effectively in realizing your design. This provides the added benefit of simplifying the debugging of multithreaded applications in most cases

Make your applications scale:

Properly implemented, the thread pool can enforce concurrency limits that will make your application scale.

Introduce new code while minimizing risk:

Thread pooling lets you break execution into work units that are best described as development sandboxes. Sandboxes are fun and safe! How? Thread pooling promotes loose coupling between processes and naturally separates data from process. Any coupling between processes typically happens at a well-defined data point. This can be a lot easier to maintain over time, especially in large multithreaded applications.

Design

The conceptual model of a thread pool is simple: the pool starts threads running; work is queued to the pool; available threads execute the queued work. Using templates the pool may be defined independent of the thread/work implementation (a technique known as static polymorphism).

Figure 1. Thread pool collaboration diagram

The thread pool is responsible for thread creation; threads commence execution at worker::thread_proc. Requests are queued to the thread pool; the worker prepares the request and the request is queued. When a thread is available to process the work, it may request the pending work from the thread pool with thread_pool::get_queued_status. If there is no pending work, the thread is suspended until work is available.

Chaining

While our worker implementation allows us to queue work, we can go one step further. The thread pool promised to help us break problems into discrete steps that maintain state minimizing complexity and risk while maximizing the raw power we can squeeze out of our box. However, our current implementation only allows us to queue one piece of work at a time making it cumbersome to logically group sequential work together. We also need some way of knowing when that work is done so that we might queue some more work.

Example:

Take the system down; rebuild the system data; bring the system back online.

Using the code

Initialize the thread pool you would like to use. As thread pools are parameterized singletons, there will be a thread pool instance for each type of worker used. The class global::thread_pool is a convenient typedef for core::thread_pool<core::worker_thread>.

global::thread_pool::instance().initialize();

If you choose core::worker_thread as your worker implementation, all work will be derived from core::work_unit and your work will be performed when process is called.

To queue work, create an instance of your class and initialize it as necessary. Use thread_pool::queue_request to queue the work.

// demonstrate chainingglobal::thread_pool::instance().queue_request(
(core::chain(), new work_1, new work_2, new work_3));

To shutdown the thread pool, use thread_pool::shutdown.

global::thread_pool::instance().shutdown();

About the demo program

The demo program does the following:

Initializes the global::thread_pool instance.

Instantiates three different types of work

Instantiates a chain tying the work together.

Queues and processes the work.

Shuts down the thread pool.

If you are a member of a team, you can quickly divide and distribute the work to the team to implement as work units. Each work unit can be tested independently and integrated as a final product. Each person has a sandbox to play in.

Points of interest

[1] I chose to overload the comma because it makes for nice lists – this is a useful tool for writing self documenting code.

History

24/04/2004 Article creation

License

This article has no explicit license attached to it but may contain usage terms in the article text or the download files themselves. If in doubt please contact the author via the discussion board below.

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About the Author

Joshua Emele lives in San Francisco. A member of Plugware Solutions, Ltd. and specializes in network, database, and workflow applications in c++.
He is madly in love with life and his partner and enjoys teaching, playing classical guitar,
hiking, and digital electronics.

Comments and Discussions

This is really exceptional code. But some questions remain. Why not creating thread objects in create_threads()? This could a little bit faster. Another question is the reason for implementing the threadpool as singleton. The CreateThread() method accepts a void* parameter for storing such data like the this pointer of the thread pool.

The next problem is the missing OVERLAPPED data in the execute method. OVERLAPPED data are essential during processing asynchronous socket calls.